Evaluation of sustainable distribution network design decisions for essential medical products in Turkey

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Abstract
Introduction: Optimization of distribution for essential medical products is important goal in health systems. For this purpose, the network design should consider economic, environmental, and social aspects together. This issue is especially serious in Türkiye, where regional disparities, infrastructure differences, and limited access to healthcare make pharmaceutical distribution more complicated. Methods: This study develops a tri objective mathematical programming model for sustainable distribution network design of essential medical products in Türkiye. The model tries to minimize logistics cost and carbon emission at the same time, while it maximizes social benefit through regional job creation. Since the problem is NP hard, two multi objective metaheuristic algorithms, NSGA II and MOPSO, were applied for solving large scale instances. Their performance was evaluated by mean ideal distance, spacing metric, number of Pareto solutions, and computational time. Validation was done through comparison of metaheuristic results with exact MILP solutions in small scale instances. Results: The results show that both algorithms produced near optimal solutions with good quality. MOPSO had faster convergence, lower mean ideal distance, and also more uniform Pareto front, while NSGA II gave wider spread of alternative solutions. No statistically significant difference was found in the number of non-dominated solutions. The case study results also show that the proposed model can help simultaneous improvement of economic, environmental, and social performance in Turkish healthcare distribution network. Discussion: The proposed framework gives a practical decision support basis for sustainable and resilient healthcare distribution network design in Türkiye. Its contribution is that it brings together cost, carbon emissions, and regional employment generation in one unified multi echelon optimization structure, and also shows that metaheuristic solution methods can be useful for real world healthcare logistics planning.

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